As the variants of AE, denoising autoencoders (DAEs) [1]} and variational autoencoders (VAEs) [2]} were used for top-N recommendation. CDAE [3]} utilizes a DAE by corrupting the input vector randomly. MultVAE [4]} and RecVAE [5]} applied the VAE for top-N recommendation. As the simplified version of SLIM [6]}, EASE\(^{R}\)  [7]} showed state-of-the-art results. However, the existing AE-based models focused only on developing a single global model without taking the locality in the data into account.
